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- affiliation: University College, London, UK
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2020 – today
- 2025
[j110]Dino Pedreschi
, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-László Barabási, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, János Kertész, Alistair Knott, Yannis E. Ioannidis, Paul Lukowicz, Andrea Passarella, Alex 'Sandy' Pentland, John Shawe-Taylor, Alessandro Vespignani:
Human-AI coevolution. Artif. Intell. 339: 104244 (2025)- 2024
- [j109]Nicolas Belissent, José M. Peña, Gustavo A. Mesías-Ruiz, John Shawe-Taylor, María Pérez-Ortiz:
Transfer and zero-shot learning for scalable weed detection and classification in UAV images. Knowl. Based Syst. 292: 111586 (2024) - [c155]Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman Srazali, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, Sahan Bulathwela:
A Toolbox for Modelling Engagement with Educational Videos. AAAI 2024: 23128-23136 - [i59]Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, Sahan Bulathwela:
A Toolbox for Modelling Engagement with Educational Videos. CoRR abs/2401.05424 (2024) - [i58]Yuta Nagano, Andrew Pyo, Martina Milighetti, James Henderson, John Shawe-Taylor, Benny Chain, Andreas Tiffeau-Mayer:
Contrastive learning of T cell receptor representations. CoRR abs/2406.06397 (2024) - 2023
- [j108]Kevin Baum, Joanna Bryson, Frank Dignum, Virginia Dignum, Marko Grobelnik, Holger H. Hoos, Morten Irgens, Paul Lukowicz, Catelijne Muller, Francesca Rossi, John Shawe-Taylor, Andreas Theodorou, Ricardo Vinuesa:
From fear to action: AI governance and opportunities for all. Frontiers Comput. Sci. 5 (2023) - [j107]Jamie Danemayer, Cathy Holloway, Youngjun Cho, Nadia Berthouze, Aneesha Singh, William Bhot, Ollie Dixon, Marko Grobelnik, John Shawe-Taylor:
Seeking information about assistive technology: Exploring current practices, challenges, and the need for smarter systems. Int. J. Hum. Comput. Stud. 177: 103078 (2023) - [j106]Jie M. Zhang, Mark Harman, Benjamin Guedj, Earl T. Barr, John Shawe-Taylor:
Model validation using mutated training labels: An exploratory study. Neurocomputing 539: 126116 (2023) - [c154]Simon Schmitt, John Shawe-Taylor, Hado van Hasselt:
Exploration via Epistemic Value Estimation. AAAI 2023: 9742-9751 - [e12]Paul Lukowicz, Sven Mayer, Janin Koch, John Shawe-Taylor, Ilaria Tiddi:
HHAI 2023: Augmenting Human Intellect - Proceedings of the Second International Conference on Hybrid Human-Artificial Intelligence, June 26-30, 2023, Munich, Germany. Frontiers in Artificial Intelligence and Applications 368, IOS Press 2023, ISBN 978-1-64368-394-2 [contents] - [i57]Simon Schmitt, John Shawe-Taylor, Hado van Hasselt:
Exploration via Epistemic Value Estimation. CoRR abs/2303.04012 (2023) - [i56]Dino Pedreschi, Luca Pappalardo, Ricardo Baeza-Yates, Albert-László Barabási, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, János Kertész, Alistair Knott, Yannis E. Ioannidis, Paul Lukowicz, Andrea Passarella, Alex 'Sandy' Pentland, John Shawe-Taylor, Alessandro Vespignani:
Social AI and the Challenges of the Human-AI Ecosystem. CoRR abs/2306.13723 (2023) - [i55]Theodore Wolf, Nantas Nardelli, John Shawe-Taylor, María Pérez-Ortiz:
Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models. CoRR abs/2312.06527 (2023) - 2022
- [j105]Shiliang Sun, Mengran Yu, John Shawe-Taylor, Liang Mao:
Stability-based PAC-Bayes analysis for multi-view learning algorithms. Inf. Fusion 86-87: 76-92 (2022) - [c153]Simon Schmitt, John Shawe-Taylor, Hado van Hasselt:
Chaining Value Functions for Off-Policy Learning. AAAI 2022: 8187-8195 - [c152]María Pérez-Ortiz, Sahan Bulathwela, Claire Dormann, Meghana Verma, Stefan Kreitmayer, Richard Noss, John Shawe-Taylor, Yvonne Rogers, Emine Yilmaz:
Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos? CHIIR 2022: 90-101 - [c151]Sahan Bulathwela, Meghana Verma, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. EDM 2022 - [c150]Florina-Cristina Calnegru, John Shawe-Taylor, Iasonas Kokkinos, Razvan Pascanu:
Correlation Based Semantic Transfer with Application to Domain Adaptation. ICONIP (1) 2022: 588-599 - [i54]María Pérez-Ortiz, Sahan Bulathwela, Claire Dormann, Meghana Verma, Stefan Kreitmayer, Richard Noss, John Shawe-Taylor, Yvonne Rogers, Emine Yilmaz:
Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos? CoRR abs/2201.03408 (2022) - [i53]Simon Schmitt, John Shawe-Taylor, Hado van Hasselt:
Chaining Value Functions for Off-Policy Learning. CoRR abs/2201.06468 (2022) - [i52]Reuben Adams, John Shawe-Taylor, Benjamin Guedj:
Controlling Confusion via Generalisation Bounds. CoRR abs/2202.05560 (2022) - [i51]Najiba Toron, Janaina Mourão Miranda, John Shawe-Taylor:
TransductGAN: a Transductive Adversarial Model for Novelty Detection. CoRR abs/2203.15406 (2022) - [i50]Sahan Bulathwela, Meghana Verma, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. CoRR abs/2207.01504 (2022) - [i49]Wendy E. Mackay, John Shawe-Taylor, Frank van Harmelen:
Human-Centered Artificial Intelligence (Dagstuhl Seminar 22262). Dagstuhl Reports 12(6): 112-117 (2022) - 2021
- [j104]Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor:
PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses. Entropy 23(10): 1330 (2021) - [j103]María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári:
Tighter Risk Certificates for Neural Networks. J. Mach. Learn. Res. 22: 227:1-227:40 (2021) - [j102]Arthur Gwagwa, Emre Kazim, Patti Kachidza, Airlie Hilliard, Kathleen Siminyu, Matthew Smith, John Shawe-Taylor:
Road map for research on responsible artificial intelligence for development (AI4D) in African countries: The case study of agriculture. Patterns 2(12): 100381 (2021) - [c149]María Pérez-Ortiz, Claire Dormann, Yvonne Rogers, Sahan Bulathwela, Stefan Kreitmayer, Emine Yilmaz, Richard Noss, John Shawe-Taylor:
X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI. IUI Companion 2021: 70-74 - [i48]Sahan Bulathwela, María Pérez-Ortiz, Erik Novak, Emine Yilmaz, John Shawe-Taylor:
PEEK: A Large Dataset of Learner Engagement with Educational Videos. CoRR abs/2109.03154 (2021) - [i47]María Pérez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, Josef Kittler:
Learning PAC-Bayes Priors for Probabilistic Neural Networks. CoRR abs/2109.10304 (2021) - [i46]María Pérez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernández, Benjamin Guedj, John Shawe-Taylor:
Progress in Self-Certified Neural Networks. CoRR abs/2111.07737 (2021) - [i45]María Pérez-Ortiz, Erik Novak, Sahan Bulathwela, John Shawe-Taylor:
An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All. CoRR abs/2112.01242 (2021) - [i44]Sahan Bulathwela, María Pérez-Ortiz, Catherine Holloway, John Shawe-Taylor:
Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution. CoRR abs/2112.02034 (2021) - [i43]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems. CoRR abs/2112.04368 (2021) - 2020
- [j101]Luca Oneto, Michele Donini, Massimiliano Pontil, John Shawe-Taylor:
Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy. Neurocomputing 416: 231-243 (2020) - [j100]Sahan Bulathwela, María Pérez-Ortiz, Rishabh Mehrotra, Davor Orlic, Colin de la Higuera, John Shawe-Taylor, Emine Yilmaz:
Report on the WSDM 2020 workshop on state-based user modelling (SUM'20). SIGIR Forum 54(1): 5:1-5:11 (2020) - [c148]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. AAAI 2020: 565-573 - [c147]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
Towards an Integrative Educational Recommender for Lifelong Learners (Student Abstract). AAAI 2020: 13759-13760 - [c146]Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor:
Predicting Engagement in Video Lectures. EDM 2020 - [c145]Tobias Baumann, Thore Graepel, John Shawe-Taylor:
Adaptive Mechanism Design: Learning to Promote Cooperation. IJCNN 2020: 1-7 - [c144]Jun Yamada, John Shawe-Taylor, Zafeirios Fountas:
Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning. IJCNN 2020: 1-8 - [c143]Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor:
PAC-Bayes Analysis Beyond the Usual Bounds. NeurIPS 2020 - [c142]Sahan Bulathwela, María Pérez-Ortiz, Rishabh Mehrotra, Davor Orlic, Colin de la Higuera, John Shawe-Taylor, Emine Yilmaz:
SUM'20: State-based User Modelling. WSDM 2020: 899-900 - [i42]Jun Yamada, John Shawe-Taylor, Zafeirios Fountas:
Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning. CoRR abs/2002.03267 (2020) - [i41]Yuxin Sun, Benny Chain, Samuel Kaski, John Shawe-Taylor:
Correlated Feature Selection with Extended Exclusive Group Lasso. CoRR abs/2002.12460 (2020) - [i40]Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor:
Predicting Engagement in Video Lectures. CoRR abs/2006.00592 (2020) - [i39]Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor:
PAC-Bayes unleashed: generalisation bounds with unbounded losses. CoRR abs/2006.07279 (2020) - [i38]Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor:
PAC-Bayes Analysis Beyond the Usual Bounds. CoRR abs/2006.13057 (2020) - [i37]María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári:
Tighter risk certificates for neural networks. CoRR abs/2007.12911 (2020) - [i36]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement. CoRR abs/2011.02273 (2020) - [i35]Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz, John Shawe-Taylor:
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings. CoRR abs/2012.03780 (2020) - [i34]Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor:
Upper and Lower Bounds on the Performance of Kernel PCA. CoRR abs/2012.10369 (2020)
2010 – 2019
- 2019
- [j99]Michele Donini, João M. Monteiro, Massimiliano Pontil, Tim Hahn, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda:
Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. NeuroImage 195: 215-231 (2019) - [i33]Jie M. Zhang, Earl T. Barr, Benjamin Guedj, Mark Harman, John Shawe-Taylor:
Perturbed Model Validation: A New Framework to Validate Model Relevance. CoRR abs/1905.10201 (2019) - [i32]Petru Manescu, Lydia Neary-Zajiczek, Michael J. Shaw, Muna Elmi, Remy Claveau, Vijay Pawar, John Shawe-Taylor, Iasonas Kokkinos, Mandayam A. Srinivasan, Ikeoluwa Lagunju, Olugbemiro Sodeinde, Biobele J. Brown, Delmiro Fernandez-Reyes:
Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy. CoRR abs/1906.07496 (2019) - [i31]Biobele J. Brown, Alexander A. Przybylski, Petru Manescu, Fabio Caccioli, Gbeminiyi Oyinloye, Muna Elmi, Michael J. Shaw, Vijay Pawar, Remy Claveau, John Shawe-Taylor, Mandayam A. Srinivasan, Nathaniel K. Afolabi, Adebola E. Orimadegun, Wasiu A. Ajetunmobi, Francis Akinkunmi, Olayinka Kowobari, Kikelomo Osinusi, Felix O. Akinbami, Samuel Omokhodion, Wuraola A. Shokunbi, Ikeoluwa Lagunju, Olugbemiro Sodeinde, Delmiro Fernandez-Reyes:
Data-Driven Malaria Prevalence Prediction in Large Densely-Populated Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning Approaches and 22-years of Prospectively Collected Data. CoRR abs/1906.07502 (2019) - [i30]Gaurav Singh, Zahra Sabet, John Shawe-Taylor, James Thomas:
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation. CoRR abs/1910.09255 (2019) - [i29]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. CoRR abs/1911.09471 (2019) - [i28]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
Towards an Integrative Educational Recommender for Lifelong Learners. CoRR abs/1912.01592 (2019) - 2018
- [j98]Viivi Uurtio, João M. Monteiro, Jaz S. Kandola, John Shawe-Taylor, Delmiro Fernandez-Reyes, Juho Rousu:
A Tutorial on Canonical Correlation Methods. ACM Comput. Surv. 50(6): 95:1-95:33 (2018) - [j97]Huanfa Chen, Tao Cheng, John Shawe-Taylor:
A Balanced Route Design for Min-Max Multiple-Depot Rural Postman Problem (MMMDRPP): a police patrolling case. Int. J. Geogr. Inf. Sci. 32(1): 169-190 (2018) - [j96]Kira Kempinska, Paul A. Longley, John Shawe-Taylor:
Interactional regions in cities: making sense of flows across networked systems. Int. J. Geogr. Inf. Sci. 32(7): 1348-1367 (2018) - [c141]Gaurav Singh, James Thomas, Iain James Marshall, John Shawe-Taylor, Byron C. Wallace:
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding. EMNLP 2018: 2837-2842 - [c140]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization Under Fairness Constraints. NeurIPS 2018: 2796-2806 - [c139]Omar Rivasplata, Csaba Szepesvári, John Shawe-Taylor, Emilio Parrado-Hernández, Shiliang Sun:
PAC-Bayes bounds for stable algorithms with instance-dependent priors. NeurIPS 2018: 9234-9244 - [c138]Fabio S. Ferreira, Maria João Duarte Rosa, Michael Moutoussis, Ray Dolan, John Shawe-Taylor, John Ashburner, Janaina Mourão Miranda:
Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships. PRNI 2018: 1-4 - [i27]Gaurav Singh, James Thomas, John Shawe-Taylor:
Improving Active Learning in Systematic Reviews. CoRR abs/1801.09496 (2018) - [i26]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization under Fairness Constraints. CoRR abs/1802.08626 (2018) - [i25]Tobias Baumann, Thore Graepel, John Shawe-Taylor:
Adaptive Mechanism Design: Learning to Promote Cooperation. CoRR abs/1806.04067 (2018) - [i24]Omar Rivasplata, Emilio Parrado-Hernández, John Shawe-Taylor, Shiliang Sun, Csaba Szepesvári:
PAC-Bayes bounds for stable algorithms with instance-dependent priors. CoRR abs/1806.06827 (2018) - [i23]Seth Nabarro, Tristan Fletcher, John Shawe-Taylor:
Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression. CoRR abs/1806.10873 (2018) - [i22]Gaurav Singh, John Shawe-Taylor:
Faster Convergence & Generalization in DNNs. CoRR abs/1807.11414 (2018) - [i21]Gaurav Singh, James Thomas, Iain James Marshall, John Shawe-Taylor, Byron C. Wallace:
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding. CoRR abs/1810.01468 (2018) - [i20]Luke R. Harries, Suyi Zhang, Geoffroy Dubourg-Felonneau, James H. R. Farmery, Jonathan Sinai, Belle Taylor, Nirmesh Patel, John W. Cassidy, John Shawe-Taylor, Harry W. Clifford:
Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection. CoRR abs/1811.11674 (2018) - 2017
- [j95]Shiliang Sun, John Shawe-Taylor, Liang Mao:
PAC-Bayes analysis of multi-view learning. Inf. Fusion 35: 117-131 (2017) - [j94]Simon Cousins, John Shawe-Taylor:
High-probability minimax probability machines. Mach. Learn. 106(6): 863-886 (2017) - [c137]Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski:
Localized Lasso for High-Dimensional Regression. AISTATS 2017: 325-333 - [c136]Gaurav Singh, Iain James Marshall, James Thomas, John Shawe-Taylor, Byron C. Wallace:
A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation. CIKM 2017: 1519-1528 - [i19]Viivi Uurtio, João M. Monteiro, Jaz S. Kandola, John Shawe-Taylor, Delmiro Fernandez-Reyes, Juho Rousu:
A Tutorial on Canonical Correlation Methods. CoRR abs/1711.02391 (2017) - 2016
- [c135]Guy Lever, John Shawe-Taylor, Ronnie Stafford, Csaba Szepesvári:
Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning. AAAI 2016: 1779-1787 - [c134]Michele Donini, David Martínez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil:
Distributed variance regularized Multitask Learning. IJCNN 2016: 3101-3109 - [c133]Michele Donini, João M. Monteiro, Massimiliano Pontil, John Shawe-Taylor, Janaina Mourão Miranda:
A multimodal multiple kernel learning approach to Alzheimer's disease detection. MLSP 2016: 1-6 - [i18]Diana Borsa, Thore Graepel, John Shawe-Taylor:
Learning Shared Representations in Multi-task Reinforcement Learning. CoRR abs/1603.02041 (2016) - [i17]Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski:
Sparse Network Lasso for Local High-dimensional Regression. CoRR abs/1603.06743 (2016) - [i16]Dorota Glowacka, Yee Whye Teh, John Shawe-Taylor:
Image Retrieval with a Bayesian Model of Relevance Feedback. CoRR abs/1603.09522 (2016) - [i15]Gaurav Singh, Fabrizio Silvestri, John Shawe-Taylor:
Neighborhood Sensitive Mapping for Zero-Shot Classification using Independently Learned Semantic Embeddings. CoRR abs/1605.08242 (2016) - 2015
- [j93]Maria João Duarte Rosa, Liana Catarina Lima Portugal, Tim Hahn, Andreas J. Fallgatter, Marta I. Garrido, John Shawe-Taylor, Janaina Mourão Miranda:
Sparse network-based models for patient classification using fMRI. NeuroImage 105: 493-506 (2015) - [j92]Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio:
Challenges in representation learning: A report on three machine learning contests. Neural Networks 64: 59-63 (2015) - [c132]Joao M. Monteiro, Anil Rao, John Ashburner, John Shawe-Taylor, Janaina Mourão Miranda:
Multivariate Effect Ranking via Adaptive Sparse PLS. PRNI 2015: 25-28 - 2014
- [j91]Niclas Thomas, Katharine Best, Mattia Cinelli, Shlomit Reich-Zeliger, Hilah Gal, Eric Shifrut, Asaf Madi, Nir Friedman, John Shawe-Taylor, Benny Chain:
Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence. Bioinform. 30(22): 3181-3188 (2014) - [j90]Shiliang Sun, Zakria Hussain, John Shawe-Taylor:
Manifold-preserving graph reduction for sparse semi-supervised learning. Neurocomputing 124: 13-21 (2014) - [j89]Emilio Parrado-Hernández, Vanessa Gómez-Verdejo, Manel Martínez-Ramón, John Shawe-Taylor, Pino Alonso, Jesús Pujol, José Manuel Menchón, Narcís Cardoner, Carles Soriano-Mas:
Discovering brain regions relevant to obsessive-compulsive disorder identification through bagging and transduction. Medical Image Anal. 18(3): 435-448 (2014) - [j88]Jane M. Rondina, Tim Hahn, Leticia de Oliveira, Andre F. Marquand, Thomas Dresler, Thomas Leitner, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda:
SCoRS - A Method Based on Stability for Feature Selection and Apping in Neuroimaging. IEEE Trans. Medical Imaging 33(1): 85-98 (2014) - [j87]Jane M. Rondina, Tim Hahn, Leticia de Oliveira, Andre F. Marquand, Thomas Dresler, Thomas Leitner, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda:
Correction to "SCoRS - A Method Based on Stability for Feature Selection and Mapping in Neuroimaging". IEEE Trans. Medical Imaging 33(3): 794 (2014) - [c131]Sohan Seth, John Shawe-Taylor, Samuel Kaski:
Retrieval of Experiments by Efficient Comparison of Marginal Likelihoods. ICONIP (2) 2014: 135-142 - [c130]John Shawe-Taylor:
Deep-er Kernels. ICPRAM 2014: IS-9 - [c129]João M. Monteiro, Anil Rao, John Ashburner, John Shawe-Taylor, Janaina Mourão Miranda:
Leveraging Clinical Data to Enhance Localization of Brain Atrophy. MLINI@NIPS 2014: 60-68 - [c128]Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John Shawe-Taylor:
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks. NIPS 2014: 873-881 - [c127]Dimitris Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes:
Learning Non-Linear Feature Maps, With An Application To Representation Learning. ICLR (Workshop Poster) 2014 - [i14]Sohan Seth, John Shawe-Taylor, Samuel Kaski:
Retrieval of Experiments by Efficient Estimation of Marginal Likelihood. CoRR abs/1402.4653 (2014) - [i13]Shiliang Sun, John Shawe-Taylor:
PAC-Bayes Analysis of Multi-view Learning. CoRR abs/1406.5614 (2014) - [i12]Zakria Hussain, Arto Klami, Jussi Kujala, Alex Po Leung, Kitsuchart Pasupa, Peter Auer, Samuel Kaski, Jorma Laaksonen, John Shawe-Taylor:
PinView: Implicit Feedback in Content-Based Image Retrieval. CoRR abs/1410.0471 (2014) - 2013
- [j86]Niclas Thomas, James M. Heather, Wilfred Ndifon, John Shawe-Taylor, Benjamin Chain:
Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machine. Bioinform. 29(5): 542-550 (2013) - [j85]Juho Rousu, Daniel D. Agranoff, Olugbemiro Sodeinde, John Shawe-Taylor, Delmiro Fernandez-Reyes:
Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria. PLoS Comput. Biol. 9(4) (2013) - [j84]Guy Lever, François Laviolette, John Shawe-Taylor:
Tighter PAC-Bayes bounds through distribution-dependent priors. Theor. Comput. Sci. 473: 4-28 (2013) - [c126]Kitsuchart Pasupa, Zakria Hussain, John Shawe-Taylor, Peter Willett:
Drug screening with Elastic-net multiple kernel learning. BIBE 2013: 1-5 - [c125]Steffen Grünewälder, Arthur Gretton, John Shawe-Taylor:
Smooth Operators. ICML (3) 2013: 1184-1192 - [c124]Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Chuang Zhang, Yoshua Bengio:
Challenges in Representation Learning: A Report on Three Machine Learning Contests. ICONIP (3) 2013: 117-124 - [c123]Maria João Duarte Rosa, Liana Catarina Lima Portugal, John Shawe-Taylor, Janaina Mourão Miranda:
Sparse Network-Based Models for Patient Classification Using fMRI. PRNI 2013: 66-69 - [c122]Jane Maryam Rondina, John Shawe-Taylor, Janaina Mourão Miranda:
Stability-Based Multivariate Mapping Using SCoRS. PRNI 2013: 198-202 - [i11]